Automated Plaque Detection and Classification using MRI Acute thrombus formation on disrupted/eroded human atherosclerotic lesions plays a critical role on the onset of acute coronary syndromes and progression of atherosclerosis. Pathological evidence has clearly established that it is plaque composition rather than stenotic severity that modulates plaque vulnerability and thrombogenicity. Therefore, the possibility of detecting and characterizing atherosclerotic lesions would have significant clinical implications. Among the different imaging modalities, MRI seems to be the most promising in addition to its non-invasive approach. We propose to develop an automated MRI image analysis system for detecting, measuring, and classifying atherosclerotic plaques. The proposed project will be conducted by a highly experienced team of experts in signal processing, pattern recognition, statistics, and product development in close collaboration with key cardiovascular researchers, with specific expertise in MRI imaging and atherosclerosis. Automation would establish a fast, objective (observer-independent), and standard diagnostic measure of plaque burden, allowing for comparison of results between laboratories, throughout longitudinal studies, and across different imaging equipment. In a clinical setting, this system would greatly reduce the diagnostic costs involved in measuring the degree of stenosis and detecting thrombosis-prone plaques.